Data Science for Economics and Finance: Methodologies and Applications

357 Pages·2021·10.644 MB·English

by  Sergio Consoli (editor), Diego Reforgiato Recupero (editor), Michaela Saisana (editor)

Sergio Consoli 
Diego Reforgiato Recupero 
Michaela Saisana Editors
Data Science for
Economics and
Finance
Methodologies and Applications
Data Science for Economics and Finance
Sergio Consoli • Diego Reforgiato Recupero (cid:129)
Michaela Saisana
Editors
Data Science for Economics
and Finance
Methodologies and Applications
Editors
SergioConsoli DiegoReforgiatoRecupero
EuropeanCommission DepartmentofMathematicsandComputer
JointResearchCentre Science
Ispra(VA),Italy UniversityofCagliari
Cagliari,Italy
MichaelaSaisana
EuropeanCommission
JointResearchCentre
Ispra(VA),Italy
ISBN978-3-030-66890-7 ISBN978-3-030-66891-4 (eBook)
https://doi.org/10.1007/978-3-030-66891-4
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Foreword
To help repair the economic and social damage wrought by the coronavirus
pandemic,atransformationalrecoveryisneeded.Thesocialandeconomicsituation
intheworldwasalreadyshakenbythefallof2019,whenonefourthoftheworld’s
developednationsweresufferingfromsocialunrest,andinmorethanhalfthethreat
ofpopulismwasasrealasithaseverbeen.Thecoronavirusacceleratedthosetrends
and I expectthe aftermathto be in much worse shape.The urgencyto reformour
societiesisgoingtobeatitshighest.Artificialintelligenceanddatasciencewillbe
key enablers of such transformation. They have the potential to revolutionize our
wayoflifeandcreatenewopportunities.
The use of data science and artificial intelligence for economics and finance
isprovidingbenefitsforscientists, professionals,and policy-makersbyimproving
the available data analysis methodologies for economic forecasting and therefore
makingoursocietiesbetterpreparedforthechallengesoftomorrow.
This book is a good example of how combining expertise from the European
Commission, universities in the USA and Europe, financial and economic insti-
tutions, and multilateral organizations can bring forward a shared vision on the
benefitsofdatascience appliedtoeconomicsandfinance,fromtheresearchpoint
ofviewtotheevaluationofpolicies.Itshowcaseshowdatascienceisreshapingthe
businesssector.Itincludesexamplesofnovelbigdatasourcesandsomesuccessful
applicationsontheuseofadvancedmachinelearning,naturallanguageprocessing,
networks analysis, and time series analysis and forecasting, among others, in the
economicandfinancialsectors.Atthesametime,thebookismakinganappealfor
afurtheradoptionofthesenovelapplicationsinthefieldofeconomicsandfinance
sothattheycanreachtheirfullpotentialandsupportpolicy-makersandtherelated
stakeholdersinthetransformationalrecoveryofoursocieties.
We arenotjustrepairingthedamagetooureconomiesandsocieties,theaimis
tobuildbetterforthenextgeneration.Theproblemsareinherentlyinterdisciplinary
and global, hence they require international cooperation and the investment in
collaborative work. We better learn what each other is doing, and we better learn
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vi Foreword
the tools and language that each discipline bringsto the table, and we better start
now.Thisbookisagoodplacetokickoff.
SocietyofSloanFellowsProfessorofManagement RobertoRigobon
Professor,AppliedEconomics
MassachusettsInstituteofTechnology
Cambridge,MA,USA
Preface
Economic and fiscal policies conceived by international organizations, govern-
ments,andcentralbanksheavilydependoneconomicforecasts,inparticularduring
times of economicand societal turmoillike the one we haverecently experienced
with the coronavirusspreading worldwide. The accuracy of economic forecasting
and nowcasting models is however still problematic since modern economies
are subject to numerous shocks that make the forecasting and nowcasting tasks
extremelyhard,bothintheshortandmedium-longruns.
In this context, the use of recent Data Science technologies for improving
forecastingandnowcastingforseveraltypesofeconomicandfinancialapplications
has high potential. The vast amount of data available in current times, referred
to as the Big Data era, opens a huge amount of opportunities to economists and
scientists, with a conditionthat data areopportunatelyhandled,processed,linked,
andanalyzed.Fromforecastingeconomicindexeswithlittleobservationsandonly
a few variables, we now have millions of observationsand hundredsof variables.
Questions that previouslycould only be answered with a delay of several months
or even yearscan now be addressed nearly in real time. Big data, related analysis
performedthrough(Deep) Machine Learningtechnologies,and the availability of
more and more performing hardware (Cloud Computing infrastructures, GPUs,
etc.) can integrate and augment the information carried out by publicly available
aggregatedvariablesproducedbynationalandinternationalstatisticalagencies.By
loweringthelevelofgranularity,DataSciencetechnologiescanuncovereconomic
relationships that are often not evident when variables are in an aggregated form
over many products, individuals, or time periods. Strictly linked to that, the
evolution of ICT has contributed to the development of several decision-making
instrumentsthathelpinvestorsintakingdecisions.Thisevolutionalsobroughtabout
thedevelopmentofFinTech,anewlycoinedabbreviationforFinancialTechnology,
whose aim is to leverage cutting-edge technologies to compete with traditional
financialmethodsforthedeliveryoffinancialservices.
ThisbookisinspiredbythedesireforstimulatingtheadoptionofDataScience
solutions for Economics and Finance, giving a comprehensive picture on the use
ofData Science asa new scientific andtechnologicalparadigmforboostingthese
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viii Preface
sectors. As a result, the book explores a wide spectrum of essential aspects of
Data Science, spanning from its main concepts, evolution, technical challenges,
and infrastructures to its role and vast opportunities it offers in the economic
and financial areas. In addition, the book shows some successful applications on
advancedDataSciencesolutionsusedtoextractnewknowledgefromdatainorder
to improve economic forecasting and nowcasting models. The theme of the book
isatthefrontierofeconomicresearchinacademia,statisticalagencies,andcentral
banks.Also,inthelastcoupleofyears,severalmaster’sprogramsinDataScience
and Economics have appeared in top European and international institutions and
universities. Therefore, considering the number of recent initiatives that are now
pushingtowardstheuseofdataanalysiswithintheeconomicfield,wearepursuing
with the present book at highlightingsuccessful applicationsof Data Science and
Artificial Intelligence into the economic and financial sectors. The book follows
up a recently published Springer volume titled: “Data Science for Healthcare:
MethodologiesandApplications,”whichwasco-editedbyDr.SergioConsoli,Prof.
Diego Reforgiato Recupero, and Prof. Milan Petkovic, that tackles the healthcare
domainunderdifferentdataanalysisangles.
HowThisBookIs Organized
Thebookcoversthe use ofData Science, includingAdvancedMachineLearning,
Big Data Analytics, Semantic Web technologies, Natural Language Processing,
SocialMediaAnalysis,andTimeSeriesAnalysis,amongothers,forapplicationsin
EconomicsandFinance.Particularcareonmodelinterpretabilityisalsohighlighted.
This book is ideal for some educational sessions to be used in international
organizations,researchinstitutions,andenterprises.Thebookstartswith anintro-
duction on the use of Data Science technologies in Economics and Finance and
is followed by 13 chapters showing successful stories on the application of the
specific Data Sciencetechnologiesinto these sectors,touchingin particulartopics
related to: novel big data sources and technologies for economic analysis (e.g.,
SocialMediaandNews);Big Data modelsleveragingonsupervised/unsupervised
(Deep) Machine Learning; Natural Language Processing to build economic and
financial indicators (e.g., Sentiment Analysis, Information Retrieval, Knowledge
Engineering);ForecastingandNowcastingofeconomicvariables(e.g.,TimeSeries
AnalysisandRobo-Trading).
TargetAudience
The book is relevant to all the stakeholders involved in digital and data-intensive
research in Economics and Finance, helping them to understand the main oppor-
tunitiesandchallenges,becomefamiliarwiththelatestmethodologicalfindingsin
Preface ix
(Deep)MachineLearning,andlearnhow to use andevaluatethe performancesof
novelData Science and Artificial Intelligence tools and frameworks.This bookis
primarilyintendedfordatascientists, businessanalyticsmanagers,policy-makers,
analysts, educators, and practitioners involved in Data Science technologies for
Economics and Finance. It can also be a useful resource to research students in
disciplines and courses related to these topics. Interested readers will be able to
learn modern and effective Data Science solutions to create tangible innovations
for Economics and Finance. Prior knowledge on the basic concepts behind Data
Science, Economics,and Finance is recommendedto potentialreadersin orderto
haveasmoothunderstandingofthisbook.
Ispra(VA),Italy SergioConsoli
Cagliari,Italy DiegoReforgiatoRecupero
Ispra(VA),Italy MichaelaSaisana
Acknowledgments
We are grateful to Ralf Gerstner and his entire team from Springer for having
stronglysupportedusthroughoutthepublicationprocess.
Furthermore, special thanks to the Scientific Committee members for their
efforts to carefully revise their assigned chapter (each chapter has been reviewed
by three or four of them), thus leading us to largely improve the quality of
the book. They are, in alphabetical order: Arianna Agosto, Daniela Alderuccio,
Luca Alfieri, David Ardia, Argimiro Arratia, Andres Azqueta-Gavaldon, Luca
Barbaglia, KevenBluteau, LudovicoBoratto, Ilaria Bordino,Kris Boudt, Michael
Bräuning, Francesca Cabiddu, Cem Cakmakli, Ludovic Calès, Francesca Cam-
polongo, Annalina Caputo, Alberto Caruso, Michele Catalano, Thomas Cook,
Jacopo De Stefani, Wouter Duivesteijn, Svitlana Galeshchuk, Massimo Guidolin,
Sumru Guler-Altug, Francesco Gullo, Stephen Hansen, Dragi Kocev, Nicolas
Kourtellis,AthanasiosLapatinas,MatteoManca,SebastianoManzan,ElonaMarku,
Rossana Merola, Claudio Morana, Vincenzo Moscato, Kei Nakagawa, Andrea
Pagano,ManuelaPedio,FilippoPericoli,LucaTiozzoPezzoli,AntonioPicariello,
GiovanniPonti,RiccardoPuglisi,MubashirQasim,JuQiu,LucaRossini,Armando
Rungi, AntonioJesus Sanchez-Fuentes,Olivier Scaillet, Wim Schoutens,Gustavo
Schwenkler, Tatevik Sekhposyan, Simon Smith, Paul Soto, Giancarlo Sperlì, Ali
Caner Türkmen, Eryk Walczak, Reinhard Weisser, Nicolas Woloszko, Yucheong
Yeung,andWangYiru.
AparticularmentiontoAntonioPicariello,estimatedcolleagueandfriend,who
suddenlypassedawayatthetimeofthiswritingandcannotseethisbookpublished.
Ispra(VA),Italy SergioConsoli
Cagliari,Italy DiegoReforgiatoRecupero
Ispra(VA),Italy MichaelaSaisana
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